Trials / Completed
CompletedNCT06663852
ML Decision Model for G-NEC Adjuvant Therapy
Machine Learning-Based Decision Model for Optimal Adjuvant Therapy in Primary Gastric Neuroendocrine Carcinoma: a National Real-World Evidence Study
- Status
- Completed
- Phase
- —
- Study type
- Observational
- Enrollment
- 1,505 (actual)
- Sponsor
- Chang-Ming Huang, Prof. · Academic / Other
- Sex
- All
- Age
- —
- Healthy volunteers
- Not accepted
Summary
Gastric neuroendocrine carcinoma (G-NEC) is a rare and aggressive tumor originating from neuroendocrine cells in the stomach lining. It is characterized by a high propensity for recurrence and a generally poor prognosis. Due to its rarity, there is limited data and no established consensus on the optimal postoperative adjuvant therapy, making treatment decisions challenging for healthcare providers. This study is a retrospective analysis focusing on evaluating survival rates, identifying prognostic factors, and formulating treatment recommendations for patients with G-NEC. By analyzing real-world clinical data, we aim to better understand the factors that influence patient outcomes and to develop evidence-based strategies for improving survival. Our goal is to provide clinicians with valuable insights and tools to make more informed treatment decisions, ultimately enhancing the quality of care and outcomes for patients with this challenging disease.
Conditions
- Gastric Neuroendocrine Carcinoma (G-NEC)
- Postoperative Adjuvant Therapy for G-NEC
- Survival Outcomes
- Machine Learning
Timeline
- Start date
- 2024-01-01
- Primary completion
- 2024-06-01
- Completion
- 2024-06-30
- First posted
- 2024-10-29
- Last updated
- 2024-11-27
Locations
1 site across 1 country: China
Source: ClinicalTrials.gov record NCT06663852. Inclusion in this directory is not an endorsement.